Awesome Data Fusion

List of reference,algorithms, applications in RS data fusions (contribution are welcome)

Table of Contents

• Overview of Data Fusion
  • Trends
  • Basic Concept
  • Sensors
• Focus, Taxonomy
  • Research Focus
    • 1. Resolution enhancement:
    • 2. Feature/object detection:
    • 3. Data Alignment.
  • Taxonomy
• Current Challengings
  • 1. Issues Related to Radiation
  • 2. Issues related to Topology
  • 3. Issues Related to Data
  • 4. Issues Related to Method and Process
  • 5. Issues related to Physics
• Algorithms
  • 1.Spatial Resolution Enhancement Algorithms
  • 2. Spatiotemporal Fusion by Traditional Methods
  • 3. Spatio-Temporal Fusion by Neural Network
  • 4. SAR to Optical
  • 5.Registration
  • 6. Super Resolution Enhancement
  • 7. Classification
  • 8.Change Detction
• Quality Assessment
  • 1.Assessment Index With Reference Images.
  • 2.Assessment Index Without Reference Images.
• Community
  • IEEE GRSS data fusion contest
  • Discussion
• Special Issue "Multisensor Data Fusion in Remote Sensing, 2018
  • Software and Open Source Tools
    • Cloud Services
    • Useful Library
  • Database
• TERMS

Overview of Data Fusion

Trends

There are popular topics and review literatures in different periods.

1992~2000: Data fusion, spatial resolution

• Data Fusion Subpanel of the Joint Directors of Laboratories (JDL. 1991)

• Fusion of satellite images of different spatial resolutions(Linas et al.1997)

• Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications(Pohl et al.1997)

• Some terms of reference in data fusion(Wald,1999)

2001~2010: classification, unsupervision, change detection, model, multi-resolution, quality, feature extraction Wavelet transform

• #Handbook of Multisensor Data Fusion(Hall et al.2001)
• Mathematical Techniques in Multisensor Data Fusion (Hall et al. 2004)
• Multi-Sensor Data Fusion: An Introduction (Mitchell 2007)
• Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics(Thomas et al.2008)
• Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest (Licciardi et al.2009)
• Advances in Multi-Sensor Data Fusion: Algorithms and Applications (Dong et al.2009)
• Multi-source remote sensing data fusion: status and trends (Zhang et al.2010)

2011~2021:hyperspectum, deep learning, Heterogeneous, fusion framework, sparse expressions.

• A review of remote sensing image fusion methods(Ghassemian et al.2016)
• Data Fusion and Remote Sensing: An ever-growing relationship(Schmitt et al.2016)
• Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions(Zhu et al.2018)
• Spatiotemporal Image Fusion in Remote Sensing (Belgiu et al.2019)
• Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources(Zhu et al.2019)
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Basic Concept

DOD: Data fusion is a multilevel, multifaceted process dealing with the automatic detection, association, correlation, estimation, and combination of data and information from multiple source. Community: Data fusion is a formal framework in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of ‘greater quality’ will depend upon the application

Sensors

Satellite	Sensor	Spatial Resolution	Band	Revisit Cycle
NOAA	AVHRR	1.1km	VIS,NIR,TIR	12h
Terra/Aqua	MODIS	250m,500m,1000m	36bands	1d
Terra	ASTER	15m,30m,90m	14bands(VIS-TIR)	16d
Lansat	MSS	79m	VNIR	18d
Terra	MSS+TM	30m,120m	VNIR,TIR	16d
Terra	ETM+	30m,60m	VNIR,TIR	16d
Terra	OLI	30m,100m	VNIR,TIR	16d
OrbView-2	Sealifts	1km	VIR,NIR,pan	1d
SPOT	HRV	20m	3VNIR	26d
SPOT	VGT	1.15km	3VNIR+SWIR	26d
SPOT	HRG/HRS/VGT	10m		26d
ENVISAT	MERIS	300m	15(390-1040nm)	35d
Sentinel-2	MSI	10m,20m,60m	VNIR, SWIR	5days
Sentinel-1	SAR	>5m	C band	12
Sentinel-3	SLSTR,OLCI,SRAL,DORIS	~300M	optical, micro,	1d
HJ-1A/1B	UPDATING	30m		31d
TH-1	UPDATING	10m		58d
BJ-1	UPDATING	32m
CBERS-01/02	UPDATING	20m-150m		26d
ZY-1 02B	UPDATING	20m		26d
ZY-2 02C	UPDATING	10m		55d
SJ-9A	UPDATING	10m		69d
IRS-P3	WIFS/MOS	188m		5d
updating

Focus, Taxonomy

Research Focus

The application of data fusion in remote sensing is mainly divided in two scenarios:

1. Resolution enhancement:

It aims to provide higher resolution by combining multi modal data. The results are more like to be transition data, base map, or continuous time series for applications need high temporal and spatial resolution.

They are mostly:

• Based on pixel level.
• Focus on Robustness, temporal continuous

For examples:

Sub Topic	Enhancement	Applications
Super Resolution	Spatial	Transition for registration, base map
pan-sharpening	Spatial	base map
spatio-temporal fusion	Temporal and Spatial	base map, time series

Typical applications: Plant detection, weather detection, ecology, change detection, land cover, etc.

2. Feature/object detection:

It aims to improve accuracy and precision of specific task, based on complementary property of heterogeneous information in multimodality. Often, temporal continuity can be sacrificed.

They are mostly:

• End to End
• based on pixel level or decision level.
• Focus on accuracy of task (instead of fused images)

For examples:

Sub Topic	Enhancement	Application
Change detection	Accuracy of Change map	Land cover, Building..
Object detection	Accuracy of Detection	Car, Building
Segmentation	Accuracy of Classification	Land Cover, Forest

Typical Applications: change detection, land change coverage, land classification, disaster monitoring, building recognition, vehicle recognition, etc.

3. Data Alignment.

It aims to find and adjust the unlinear connections between different sensors, It could be divided into matching and co-registration, Furthermore, the problems need to be resolved could be regarded as topological and radiation issues. They are mostly:

• Necessary for all multi-modal applications
• part of preprocess
• can be selectively ignored or modified(such as adaptive registration)

For examples:

Sub Topic	Description
Radiometric Correction	Atmospheric effect. etc
Geometric Correction	Camera, Solar Angle etc
Band Adjustment	Function based on prior knowledge
BRDF Adjustment	Function based on vegetation
Registration of Different Modal	SAR-Optical, Multi-Temporal..
Adaptive Registration	Domain, Manifold, Attention Mechanics, Tensor

Taxonomy

Depending on the main problem solved by the model/algorithm, the development of data fusion can be divided into many areas

• According to process levels: Pixel, Attribute, Decision
• According to Principle: Weighted function based, Decomposition based, Learning based (including sparse dictionary, DL), Bayesian based, Hybrid based
• According to modal: Homogeneous fusion, Heterogeneous fusion (NIR,-VIS, Optical-SAR, Optical-Thermal, hyper-Multi etc.,), Remote sensing site fusion, Remote sensing non-observation fusion (Data assimilation)
• According to mechanisms: Competitive integration, Complementary integration(like temporal and spatial), Cooperative integration(like 3D reconstruction)
• According to Dimension: Spatial Dimension Enhancement, Spectral Dimension Enhancement, Time dimension enhancement, End to End.
• According to Applications: spatial resolution enhancement, Matching and co-registration of multisource data, Change detection, Object recognition, Agriculture, Ecology etc.,
• According to procedure: Matching, Co-registration, Process, Quality Assessment.

Current Challengings

In almost every small direction (as shown in the previous subsection). There are a number of issues so authors only list some most important challenges to demonstrate.

As mentioned before, spatial enhancement and fusion of complementary information are main scientific focus. Apart from these, there are:

1. Issues Related to Radiation

Issues	Popular Solutions	Description
Atmospheric effect	Radiometric Correction	Commonsense
Solar azimuth and elevation	Radiometric Correction	Commonsense
Band pass Adjustment	linear regression	The small differences between MSI and OLI equivalent spectral bands need to be adjusted.
Bidirectional Reflectance Distribution effect	BRD Function (BRDF)	The BRDF is needed in remote sensing for the correction of view and illumination angle effects (for example in image standardization and mosaicking)

2. Issues related to Topology

Issues	Popular Solutions	Description
Lens Distortion	(Camera Calibration in) Geometric Correction	Commonsense
Error caused by Elevation(SAR and optical)	Co-Registration	Please see figure1 and figure2
Mis-matching in pixel	Co-Registration	in Multi-Temporal, Multi-Sensor, Multi-Angle
Mis-matching in Feature	Co-Registration , Domain Adaptation	adaptive alignment as part of model
Mis-matching in Object	Co-Registration , Attention Mechanics	adaptive alignment as part of model

Figure1. Error due to deviation in DEM, Relationship between error and Angle

3. Issues Related to Data

Noise:

Issues	Popular Solutions	Description
Noise in SAR	(Camera Calibration in) Geometric Correction	Speckle
Cloud	mask, super resolution, reconstruction, interpolation

Sample:

Issues	Popular Solutions	Description
Spectral/Index Colinearity, Similarity	PCA, Correlation Analysis	Huges Phenomenon, Statistics
Lack of Samples	Data Augmentation, Semi-supervised, GANs	Overfitting, low generalization
Unbalanced Samples	Loss functions, updating...	weak train in small class
Gap in Resolution	Transition(Super resolution)	For 1:2 or higher resolution ratio in multi modal, It will increase the difficulty in data fusion

4. Issues Related to Method and Process

Issues	Popular Solutions	Description
Computation Speed	Alternative method, cloud service, Parallel computing
Issues related to training	AI tools	like overfitting, vanished gradient etc.,
Hard to fuse heterogeneity	Pixel(like unmixing), feature(like feature layers), decision level(like DT)	multi-modal
low Generalization	Data augmentation, Domain Adaptive, Pre-train	transferability
lack of Interpretability	Combination with prior knowledge(Branch, Attention, feature layers, data assimilation )	Physics

5. Issues related to Physics

Issues	Popular Solutions	Description
Intra-class Variation	(updating)	Small difference between different class
Inter-class variation	(updating)	Large difference in same class
Landscape Heterogeneity	Learning based method,(updating)	Variations in high resolution pixel.
Change due to Landcover	Learning based method,(updating)
Abrupt Change	Function related to time(updating)	disaster etc
Seasonal Change	Function related to time(updating)	vegetation

Algorithms

There are currently more than 200 spatio-temporal models, so only part of baseline models or popular papers are included.

1.Spatial Resolution Enhancement Algorithms

Multisensor image fusion for spatial resolution enhancement such as pan-sharpening, multi/hyperspectral image fusion, and downscaling of multiresolution imagery

Purpose	Principle	Method	Paper	Code	Features
Spatial	Component (+)	Principle Component Analysis(PCA)	Shettigara et al.1992	Updating
Spatial	+	Intensy-Hue-Saturation(IHS)	Tu et al.2001	Updating
Spatial	+	Brovey Transform(BT)	Tu et al.2005	Updating
Spatial	+	Gram-Smidt(GS)	Aiazzi et al.2007	Updating
Spatial	+	GS adaptive(GSA)	Aiazzi et al.2007	Updating
Spatial	+	GIHS adaptive(GIHSA)	Aiazzi et al.2007	Updating
Spatial	Unmaxing	MMT	(Zhukov et al.1999)	Updating
Spatial	+	MMT(MERIS,Lansat)	(Milla et al.2008)	Code	constraints, Positive of End Member
Spatial	+	LAC-GAC NDVI	(MAselli, 2011)	Code
Spatial	Baysian	BME	(Li et al.2013)	Code
Spatial	Hybrid	Updating
Spectral	Linear	Wavelet Transform	Nunez et al.1999	Updating
Spectral	+	High-pass filtering	Chavez et al.1991	Updating
Spectral	+	Curvelet Transform	Nencini et al.2007	Updating
Spectral	+	Contour Transform	do and Vetterli 2005	Updating
Spectral	+	Laplacian Pyramid	Schmitt and Zhu, 2005	Updating
Spectral	+	Smoothing Filter-based intensity modulation	Liu,2000	Updating
Spectral	Unmixing	Spectral Unmixing	Bendoumi et al.2014
Spectral	+	Nonnegative Matrix Unmixing	Huang et al.2008	Updating
Spectral	+	Coupled Nonnegative Matrix Unmaxing	Yokoya et al.2012	Updating
Spectral	Bayesian	Maximum a posteriori	Hardie et al.2004	Updating
Spectral	Learning	Sparse Representation
Spectral	+	Analysis Sparse Model
Spectral	+	MRA DNN	Azarang et al.2017	Code
Spectral	+	PNN	Li et al.2012	Updating
Spectral	+	DRPNN	Wei et al.2017 Matlab	Residual Network
Spectral	+	MSDCNN	Zhou et al.2019	Python
Spectral	Hybrid

2. Spatiotemporal Fusion by Traditional Methods

(Normally Prediction by fusing two high temporal and high spatial resolution sensors with correction,Multisensor and multimodal data fusion using a variety of sensors such as optical imaging, SAR, and LiDAR)

Sensor	Principle	Method	Paper	Code	Features	Registration
Landsat&MODIS	Weighted Function	STARFM	（Gao et al.2006)	Python
+	+	STAARCH	(Hilker et al.2009)	Code	Cloud and Snow Cover
+	+	ESTARFM	(Zhu et al.2010)	IDL,Python	Enhancement in Heterogeneous region
MODIS&landsat	+	RWSTFM	(wang et al.2017)	Code	kriging Based
1MODIS,2landsat	+	Prediction Smooth Method	(Zhong et al.2018)	Code	abrupt change, phenology	Manual
MODIS&landsat	+	SADFAT	Weng et al.2014	Code	Consider Annual temperature Cycle
Unlimited	+	STITFM	Wu et al.2014	Code	Multi-Sensor LST
	+	STVIFM	Liao et al.2017	Code	growth stages
	Unmixing	ESTDFM	(Zhang et al.2013)
	+	MSTDFA	Wu et al.2015	Code	sensor adjustment by linear model
	+	OB-STVIUM	(Lu et al.2016)	Code	Consideration of Phenology Change
Temporal	Bayesian	NDVI-BSFM
	Learning	SPSTFM	(Huang et al.2012)	Code	sparse representation
	+	One-pair image learning method	(Song et al.2012)	Code	sparse representation
Landsat&AHVRR	+	EBSCDL	Wu et al.2015
Landsat&MODIS	+	ELM	(Liu et al.2016 )	Code	Matlab
Landsat&MODIS	+	CSSF	(Wei et al.2017)	Code	Compression downsampling
Landsat&MODIS	+	WAIFA	Moosavi et al.2015		wavelet&ANN
2Landsat&1MODIS	+	STFDCNN	Song et al.2018	Code	Transition Image
	+	DCSFTN		Code
	Hybrid	FSDAF	(Zhu et al.2016)	Abrupt Change
	+	NDVI-LMGM	(yu et al.2015)	linear growth&unmixing
Two Time series	Others	STAIR	(Luo et al.2015)	Difference, Cloud
Multi Time series	Others	STAIR2	(Luo et al.2020)

3. Spatio-Temporal Fusion by Neural Network

Registration	Modal	Process Level	Method	Paper	Code	Features
Traditional	A(T1,T3), B(T2)	pixel	StfNet(Two Branch)	(Liu,2019)	Code	Features
Traditional	A1-3,B1,3	pixel	Super Resolution, weighted function	(SONG, 2019)	Code	Features
Traditional	A1-3,B1,3	pixelLevel	Super Resolution, weighted function	Li,2019	Code	Features
Traditional	A12,B1	PIXEL	DMnet, concatenate	lI 2020	Code	Features
Traditional	A12,B1	PIXEL	CNN, concatenate	[Yin 2020](	Traditional	A1,B1

4. SAR to Optical

Task	Source	Method	Paper	Code	Features
Pan-Sharpening	Low&High	CNN	jin et al.2016	Code	Resolution enhancedment of MSS
Pan-Sharpening	Low&High	CNN	G Masi et al.2016	Code	End-to-End
SAR 2 Optical	SAR	GANs	Reyes et al.2018	Code	Feature Level, two Stream, semi-auto- Label
SAR 2 Optical(Cloud removal)	SAR	DRN	Meraner et al.2018	Code	cloud removal
SAR 2 Optical	SAR	sar2opt	Toriya et al.2019	Code
Growth Prediction	Multi	CNN	Scarpa et al.2018	Code	pixel level, Sentinel,NDVI fusion and pixel fusion
Growth Prediction	SAR&VNIR	Random Forest	hECKEL et al.2020	Code	pixel level, Sentinel

5.Registration

Learning	Modal	Process Level	Method	Paper	Code	Features
Registration and SR	Low&High	Deep Neural Network	Y. Qu et al.2018	Code
Registration	SAR&Optical	Deep Neural Network	Mou 2017 et al.2018	Patch-based
Recognition	Hyper&SAR	Dual DCNN	Lagrange et al.2018	Code	Feature Level, two Stream
Recognition	Multi&SAR	Multi-TaskUNet	JIAN et al.2019	Code	Multi-Task(Edge, Biniary),xception
Registration	Optical&SAR	Siamese	Mou et al.2018	Code	Feature Level, two Stream, semi-auto- Label
Registration	Optical&SAR	3 DNN	Hughes et al.2020	Code	hot map, goodness
Registration	Optical&SAR	Siamese& Gaussian pyramid coupling quadtree	He et al.2018	Code	Coarse-finer
Registration	Optical&SAR	Pseudo-Siamese CNN	Hughes et al.2018	Code
Supervised	Optical&SAR	Siamese CNN	Merk et al.2017	Code

6. Super Resolution Enhancement

Registration	Modal	Process Level	Method	Paper	Code	Features
	CNN	Yuan et al.2016	Code	End-to-End,Transfer Model from CV, hyperspectrum
	Low&High	Deep Residual Convolutional Neural Network	Wang et al 2017	Code	End-to-End,Transfer Model from CV, hyperspectrum
	Low&High	SSF-CNN	X. Han et al 2018	Code	End-to-End,Transfer Model from CV, hyperspectrum
	Low&High	Deep Neural Network	R. Dian et al.2017	Code
	Low&High	Sparse Dirichlet-Net	Y. Qu et al.2018	Code
	Low&High	Deep Neural Network	Y. Qu et al.2018	Code
	Low&High	Deep Neural Network	Y. Qu et al.2020	Code	Chen
Mannual	HSI-MSI	patch-based,concatenation	Super Resolution	Low&High	Deep Neural Network	Yang et al.2018

7. Classification

Registration	Modal	Process Level	Method	Paper	Code	Features
Classification	Multi		CNN	Lagrange et al.2018	Code	Comparision of existing
Classification	Multi		FusioNet	Hu et al.2017	Code	Feature Level, two Stream
	Multi		DeepNetsForEO	Audebert et al.2017 # Semantic Segmentation of Earth Observation Data Using Multimodal and)	Code	Feature Level, two Stream
Classification	VNIR-DSM		DeepUNet	Audebert et al.2017	Code	Channel Packing
Manual	Sentinel2,Lansat8, OSM,etc,.	Pixel(Concatenation)	ResNet	Qiu et al.2018		LCZ maps

8.Change Detction

Modal	Method	Paper	Code	Features
SAR1 (T1, T2)	Ratioing/log Ratioing	Papers
SAR1 (T1, T2)	Small wavelet transform	(Bovolo,2005)		Unsupervised
SAR1 (T1, T2),SAR2 (T1, T2)	Markov	(Solarna,2018)		Unsupervised
More Change detection research please refer to Awesome Change Detection

Quality Assessment

1.Assessment Index With Reference Images.

Index	Description	Dimension	Reference
Spectral angle	updating	spectral	Updating
General image quality index	updating	spectral	Updating
Root Mean Square Error	updating	spectral&temporal	Updating
Relative mean spectral error	updating	spectral&temporal
Signal-to-noise ratio	updating	spatial	Updating
Peak signal-to-noise ratio	updating	spatial	Updating
Correlation coefficient	updating	spatial&temporal	Updating
Structural similarity coefficient		spatial&temporal	Updating
Global integrated error index	updating	spatial&spectral	Updating
Average error	updating	spatial	Updating

2.Assessment Index Without Reference Images.

Index	Description	Dimension	Reference
Average value	updating	spatial,spectral
Variance	updating	spatial,temporal	Updating
Standard deviation	updating	spatial,temporal	Updating
Information entropy	updating	spatial,temporal	Updating
Mean gradient	updating	spatial,temporal	Updating

Community

IEEE GRSS data fusion contest(Link)

Year: 2020 Title: Global Land Cover Mapping with Weak Supervision Data: MSS, SENTINEL MSS

Track1:

Main Author	Approach	Code
Robinson	A combination of iterative clustering and epitome representations	Code
Yu Xia	Multi-branch fusion of unsupervised multi-resolution segmentation, random forest classification of remote sensing indexes, and convolutional neural network predictions with post-processing based on expert priors	WHU_YuXia
Daniele Cerra	Automated label pre-processing, a Gaussian Naive Bayes classifier trained on cluster centroids, and classes obtained by k-means clustering and random forests with bag of words features, followed by classification refinement designed for specific classes	Pineapples
Track 2:
Main Author	Approach	Code
--	--	--
Huijun Chen	An ensemble of random forests trained on refined labels	Antonia
Daniele Cerra	As Track 1 third, but random forests trained on high-resolution labels for validation data	Pineapples
Shuting Yin	A combination of random forests, k-means, and DeepLabv3++ with postprocessing and retraining	dfchen

Year: 2019 Title: Reconstruct both a 3D geometric model and a segmentation of semantic classes for an urban scene Data: WorldView-3, VIR, NIR, LiDAR

Track 1: Single-view semantic 3D

Main Author	Approach	Code
Saket Kunwar	An ensemble of random forests trained on refined labels	nest
Zhuo Zheng	A pyramid on pyramid network based on an encoder-dual decoder framework	RSIDEA-WHU
Track 2: Pairwise semantic stereo
Main Author	Approach	Code
--	--	--
Hongyu Chen	A modified version of Pyramid Stereo Matching Network (PSMNet) and Disparity Fusion Segmentation Net (DFSN)	BurningAllthing
Rongjun Qin	U-Net and Pyramid Stereo Matching Network (PSMNet)	qin.324

Track 3: Multi-view semantic stereo

Main Author	Approach	Code
Pablo d’Angelo	Semi-global matching and an ensemble of CNN classifiers with ad hoc detectors	Panoptes
Rongjun Qin	Semi-global matching and U-Net	qin.324
Track 4: 3D point cloud classification
Main Author	Approach	Code
--	--	--
Lian Yanchao	An ensemble of random forests trained on refined labels	nest
Jia Meixia	Attention-SIFT Net (AS net) based on Pointnet++ and PointSIFT	aijinli0613

Year: 2018 Title: urban land use and land cover classification Data: Hyper, Multi, Lidar, RGB(5cm) updating

Main Author	Approach	Code
Yonghao Xu	Fully convolutional networks and post-classification with topological relationships among different objects	Gaussian
Daniele Cerra	Deep convolutional and shallow neural networks on a simplified set of classes, completed by a series of specific detectors and ad hoc classifiers	dlrpba
Sergey Sukhanov	Ensemble learning based on several classifiers, including convolutional neural networks, gradient boosting machines, and random forests, followed by post-processing techniques	AGTDA

2017,2016,2015 are updating!

Discussion

• Special Issue "Multisensor Data Fusion in Remote Sensing, 2018"

Software and Open Source Tools

We will focus on cloud computing and some important machine learning libraries

Cloud Services

• Google Earth Engine
• AWS
• Updating

Useful Library

• Updating

Database

• CAVE dataset
• Harvard dataset
• iCVL dataset
• NUS datase
• NTIRE18 dataset
• Chikusei dataset
• Indian Pines, Salinas, KSC et al.
• Sen1-2 Dataset
• SEN12MS

TERMS

There are terms which are slightly different from those in other areas.

Measurements（SIGNAL/image）: Primarily the outputs of a sensor，represent the raw information, normally in format of singal, images. The elementary support of the measurement is a pixel in the case of an image, and is called a sample in the general case

Object: It is defined by its properties, e.g., its color, its materials, its shapes, its neighborhood, etc. It can be a field, a building, the edge of a road, a cloud, an oceanic eddy, etc.

attribute(Feature): It is a property of an object. Mathematical attribute: aggregation of measurements made for each of the elements of the object Modality: It refers to the raw input used by the sensors.

Spatial context of a pixel, computed by local variance, or structure function or any spatial operator. This operation can be extended to time context in the case of time-series of measurements. Equivalent terms are local variability, local fluctuations, spatial or time texture, or pattern.